Data Analytics in Healthcare: Challenges and Uses

As healthcare continues to evolve, so does the complexity and volume of generated data. If harnessed correctly, this data can be a game-changer in improving patient care and optimizing operational efficiency. Gain a deeper understanding of healthcare data analytics, discover methods to overcome implementation challenges, and learn how data analytics revolutionizes the healthcare industry.

An enormous amount of data is being generated every day in the healthcare industry, from patient records to clinical research and administrative data. This data comes from a variety of sources, including electronic health records (EHRs), medical imaging equipment, wearable devices such as smartphones and watches, and other digital tools. 

To be useful beyond its initial application, this healthcare data needs to be further analyzed. Currently, there’s a great need for data analytics in the healthcare industry. Clinicians and researchers can leverage data analytics to identify hidden patterns and trends within clinical data, which can then help provide better patient care, improve treatment effectiveness, identify opportunities for cost savings, and drive innovation. 

While data analytics have enormous potential in the healthcare industry, it can be difficult to collect and use healthcare data due to a variety of challenges. In this article, we’ll explore opportunities and challenges of data analytics in healthcare, as well as methods being developed to address them.

What Is Healthcare Data Analytics?

What is healthcare analytics? First, it’s important to define what we mean by data analytics in healthcare. Healthcare data analytics involves the analysis of healthcare-related raw datasets to uncover hidden patterns and trends. This process can lead to improvements at both the patient and business levels.

There is more than one type of healthcare analytics. The top categories of data analytics in healthcare include:

1. Descriptive analytics: This type of analytics uses historical patient data to identify trends and benchmarks in healthcare. Descriptive analytics help us gain an understanding of past events and answer questions such as how many patients were hospitalized, what is the average BMI (Body Mass Index) for a given patient population, or what treatment plan was prescribed for a specific disease or symptoms. Simple statistical measures like counts, percentages, averages, and standard deviations are used to answer these questions. 

2. Predictive analytics: This type of analytics uses modeling and forecasting to predict future events or trends in healthcare such as which patients are likely to have the highest risk of hospitalization, or which patients may switch from home therapy to in-center care. Data scientists use historical data to train models and predict future events, employing advanced computational techniques such as machine learning. By using predictive models, we can calculate risk scores for each patient and identify which patients may require additional attention. However, there are also some problems with predictive analytics in healthcare and this will be discussed later.

3. Prescriptive analytics: This type of analytics uses machine learning to identify potential solutions to healthcare problems. Prescriptive analytics using machine learning in healthcare can be considered a more advanced technique that takes predictive analytics a step further. It suggests specific actions that can be taken to change the predicted outcome and make more informed decisions. 

4. Diagnostic analytics: This type of analytics helps us understand why events occurred, allowing us to answer questions such as why patients went to the hospital or why patients left home therapy. By investigating the data further, we can gain a better understanding of the answers found in descriptive analytics. Diagnostic analytics involves an investigation of historical data, and statistical techniques are used to identify reasons for certain events.

Each of these types of data analytics requires different tools and methods, and each has its own practical applications for healthcare organizations and patients. 

The Challenges of Data Analytics in Healthcare

Data analytics has become an essential tool for improving patient outcomes and driving innovation in the industry. However, there are significant challenges to implementing effective data analytics for healthcare organizations. 

Understanding the challenges of using patient data in clinical data analytics is critical for realizing the potential benefits of data analytics, protecting patient privacy and data security in the healthcare industry, and having the necessary skills and expertise to implement effective data analytics solutions. Below, we will examine data challenges in healthcare.

  • Data Structure Issues

In healthcare, most data is unstructured, often stemming from natural language processing. This data is typically fragmented, dispersed, and lacks standardization. Even within the same organization, aggregating and analyzing unstructured data is challenging. Electronic health records (EHRs) are notorious for their lack of interoperability across organizational lines, further complicating data sharing and analysis. Therefore, unstructured data presents a significant hurdle for healthcare professionals attempting to leverage data analytics to improve patient outcomes and healthcare operations.

  • Missing Data and Data Sparsity

In healthcare, missing electronic medical record (EMR) data can arise due to insufficient data collection or lack of documentation. The former occurs when patients are not screened for a specific medical feature, while the latter occurs when patients are screened but either their outcomes are negative and therefore not recorded or due to human errors. Additionally, data sparsity exacerbates the problem, as patients only visit the hospital a few times and only a few medical features are typically recorded. 

Fortunately, there are several techniques for handling missing data and sparsity, such as imputation methods. These methods can be categorized into two groups: those assuming missing data occur at random, including simple methods like mean imputation and advanced techniques like maximum likelihood and multiple imputations, and those assuming missing data occur not at random, such as selection models and pattern-mixture models.

  • Security Issues

Data analytics in healthcare pose significant privacy concerns, particularly with the enactment of the Health Insurance Portability and Accountability Act (HIPAA) legislation. Making healthcare data available on open-source platforms increases its vulnerability to potential breaches. Additionally, given the sensitive nature of healthcare data, confidentiality concerns are heightened. The centralized storage of this data also leaves it highly susceptible to cyber-attacks. Therefore, ensuring the privacy and security of data is of utmost importance when implementing healthcare data analytics solutions.

  • Data Standardization Issues

Healthcare organizations typically use electronic health record (EHR) systems to store and share patient data within their own network. However, the lack of standardization and compatibility between EHR platforms within an organization and outside of it poses a significant challenge for data analytics. 

Data stored in non-compatible formats and lack of standardization make data acquisition and cleansing a complicated process. These issues create limited interoperability, which is a significant challenge for data analytics in healthcare. As data is rarely standardized, healthcare data analytics faces challenges in acquiring and cleansing data into a uniform format to enable global sharing and analysis. As data continues to globalize, data analytics will need to overcome barriers of language, varied standards, and different terminologies.

Various standards development organizations, such as Health Level Seven International (HL7), developed standards that provide for global health data interoperability with the goal of solving the lack of data standardization in healthcare. HL7 standards, such as FHIR (Fast Healthcare Interoperability Resources), which has shown a significant adoption rate, promise to bring healthcare organizations to a single data standard, thus eliminating data standardization issues. 

  • Data Irregularity

EMR data presents a challenge for effective analytics due to its irregularity, which is caused by the fact that patients’ EMR data is recorded only during a hospital visit. This results in a “longitudinal patient matrix” structure, where one dimension represents medical features and the other represents time, with consecutive patient records scattered within uneven-spaced time spans. This makes it difficult to analyze and extract insights from the data, as the granularity of medical records and time periods between visits vary significantly for different patients.

To address this issue, various methods have been developed. One approach involves using baseline features to establish a common baseline for patients’ medical history, which allows for better comparisons between patient groups. Another approach is data transformation, which involves converting the irregular EMR data into regular time-series data using methods such as interpolation or imputation. The final approach involves directly using the irregular data and developing models that can work with the unevenly spaced time series data. Another crucial aspect of data transformation is converting the standardized time-series data into the Unified Code for Units of Measure (UCUM) format, ensuring consistency in the measurement units used across different healthcare records and systems. Each method has its own advantages and limitations, and the choice of method depends on the specific needs of the analysis.

  • Biases in Data 

Data bias is a significant issue in healthcare data analytics that must be addressed. One type of bias is known as biased sampling, which is dependent on the patient’s state of health and the doctor’s judgment of the patient. This results in patients being sampled more frequently when they are sick but less frequently when they are relatively healthy.

Other sources of bias can also impact the quality of EMR data, such as patients seeking medical help from different healthcare organizations that do not share information. Patients may also fail to follow up with the entire medical examination process, leading to incomplete or inaccurate data. Furthermore, the data recorded in a single specific healthcare organization may be incomplete, which may lead to biased conclusions when analyzing the data collected by that organization.

  • Data Storage and Transfers

Data generation is inexpensive compared with the storage and transfer of the same. Once data is generated, the costs associated with securing and storing them remain high. Costs are also incurred with transferring data from one place to another as well as analyzing it. 

Some researchers have combined data structure, storage, and transfers when they illustrate how structured data can be easily stored, queried, analyzed, and so forth, but unstructured data is not as easily manipulated. Cloud-based health information technology has the additional layer of security associated with the extraction, transformation, and loading of patient-related data. The use of data analytics should address issues related to increased expenditures as well as the transmittance of secure or insecure information.

One of the challenges of data analytics and data mining in healthcare is effectively managing and analyzing unstructured data, which requires advanced techniques for text mining, natural language processing, and image recognition to derive meaningful insights from sources such as clinical notes, research articles, and medical images.

Benefits of Data Analytics in Healthcare

A list of applictions of healthcare data analytics that benefit the industry.

Data analytics and healthcare services can be combined to address every aspect of patient care and operations management. Below, we’ll examine the use of analytics in healthcare with some examples and how they impact different areas of healthcare services.

Improving the quality of healthcare services:

Improving the quality of patient care necessitates the implementation of various measures, such as utilizing decision support systems that operate on large datasets to evaluate medical diagnoses and treatment approaches’ accuracy. In addition, it requires identifying more cost-effective and efficient ways of diagnosing and treating patients. Analyzing vast amounts of data can yield practical insights useful for introducing new health services, predicting disease incidence, and preventing and overcoming crises.

By examining trends that contribute to improving health and lifestyle in the population, healthcare providers can also identify areas of focus for interventions, ultimately enhancing patient outcomes. Moreover, data analytics can be used on the human genome to provide insights into personalized treatment options, improving the precision of medical care while minimizing risks associated with medical procedures. All these measures collectively contribute to enhancing the overall quality of healthcare services, benefiting patients and healthcare providers alike.

Supporting the work of medical personnel

In healthcare, the use of data analytics and intelligent data solutions is critical for supporting clinicians in various ways. For instance, by comparing current medical cases to past cases, doctors can improve diagnosis and treatment adjustments, leading to better patient outcomes. 

Data analytics also allows for the early detection of diseases, which enables prompt medical intervention. Moreover, data analytics can detect epidemiological risks and improve control of pathogenic spots and reaction rates, promoting better public health outcomes. 

By collating data on the history of the most common diseases, data analytics can identify patients predicted to have the highest risk of specific, life-threatening diseases, enabling personalized medicine and health management of individual patients and the whole population. 

Analysis of patient profiles can identify individuals for whom prevention should be applied, promoting a proactive lifestyle change or preventive care approach. Data analytics plays an essential role in predicting the occurrence of specific diseases or the worsening of patients’ results, predicting disease progression and its determinants, and estimating the risk of complications.

The importance of data analytics in healthcare for supporting the work of medical personnel cannot be overstated. However, the effectiveness of data analytics is dependent on the quality of the underlying data, making data quality a critical factor in leveraging these benefits.

Supporting scientific and research activity

Advanced data analytics supports scientific and research activity in numerous ways, enabling researchers to work more efficiently and effectively. For instance, data analytics can aid in the development of new drugs and the conduct of clinical trials by analyzing all available data, rather than relying on a limited test sample. This can facilitate the identification of patients with specific biological features who would be suitable for specialized clinical trials. Additionally, data analytics can help researchers select a group of patients who are most likely to benefit from a tested drug with minimal side effects. Furthermore, modeling and predictive analysis can be used to design better drugs and devices, leading to improved patient outcomes. Overall, data analytics is a valuable tool for advancing scientific and research activity in the medical field.

Business and management

In business and management, data analytics has proven to be a valuable tool for reducing costs and preventing abuse and fraudulent practices. With the ability to quickly identify incorrect or unauthorized financial operations, organizations can prevent abuse and eliminate errors more effectively. 

Additionally, data analytics can aid in increasing profitability by identifying patients who generate high costs and pinpointing doctors whose treatment methods are expensive. By offering solutions that reduce costs, organizations can optimize their financial performance.

Data analytics can also help identify unnecessary medical activities and procedures, such as duplicate tests, which can lead to a significant reduction in costs. This not only benefits organizations financially but also ensures that patients receive the necessary care and treatment without incurring unnecessary expenses. 

The impact of data analytics in healthcare has been significant in optimizing business operations and improving financial performance in healthcare organizations.

Conclusion

Data analytics in healthcare has valuable potential to significantly improve the ability of healthcare providers to deliver more personalized and effective patient care, as well as to identify and address emerging health trends and challenges. The growing availability of diverse data and the development of new approaches to tackle the data analytics implementation challenges faced by healthcare organizations have led to an expansion of the role of data analytics in healthcare. This, in turn, has resulted in further improvement in patient outcomes and the quality of medical procedures and treatment plans.

Are you looking to develop a smart FHIR-based analytics tool? Our team previously worked on data analytics solutions for healthcare and is ready to help you realize the full potential of intelligent data. Our Kodjin FHIR Server also offers capabilities to validate FHIR data to help prepare it for further analysis. Get in touch with us for a free consultation!

FAQ

What types of data are used in healthcare analytics?

In healthcare analytics, various types of data are used to gain insights and drive decision-making. Some of the common types of data used in healthcare analytics include: Electronic Health Records (EHR), various clinical data, claims and billing data, medical imaging data, genomic data and sensor data.

What are the promises of big data analytics in healthcare?

Big data analytics in healthcare holds great promises in improving clinical outcomes, enhancing operational efficiency, enabling personalized medicine, supporting population health management, facilitating predictive analytics, and aiding in fraud detection and prevention. By harnessing the power of large and diverse datasets, healthcare organizations can gain valuable insights, identify patterns, and make data-driven decisions that ultimately lead to better patient care, optimized processes, cost savings, and improved overall public health outcomes.

What are the examples of healthcare data analytics solutions?

Healthcare data analytics solutions encompass a range of applications. Here are a few examples:

Clinical Decision Support Systems (CDSS)

CDSS leverages patient data and medical knowledge to provide clinicians with evidence-based insights and recommendations at the point of care. It can assist in diagnosis, treatment planning, and medication selection, ensuring that clinicians have access to the latest research and best practices.


Disease Surveillance and Outbreak Detection

Healthcare organizations can employ data analytics to monitor population health trends, detect disease outbreaks, and identify potential epidemics. By analyzing data from sources such as electronic health records, social media, and public health databases, patterns and anomalies can be identified, allowing for timely interventions and resource allocation.



Fraud Detection and Prevention

Healthcare data analytics can be used to identify fraudulent activities in insurance claims, such as billing fraud or prescription abuse. By analyzing large volumes of claims data and identifying unusual patterns or anomalies, healthcare organizations can detect and prevent fraudulent behavior, leading to cost savings and preserving the integrity of healthcare systems.

How can healthcare organizations ensure the ethical use of data analytics?

Healthcare organizations can ensure the ethical use of data analytics by implementing several measures. First, they should prioritize data privacy and security, ensuring that patient information is protected and complying with relevant privacy regulations, most notable being HIPAA and GDPR.

Informed consent should be obtained from patients for data collection, use, and sharing, promoting transparency and respect for individual autonomy. Organizations should anonymize and de-identify data to protect patient privacy while allowing analysis. Establishing robust governance frameworks, policies, and procedures can ensure ethical practices and compliance with laws and regulations. 

Additionally, organizations should promote responsible data use by validating and monitoring algorithms for fairness and transparency, addressing biases, and fostering collaborations to promote mutual benefits and responsible data sharing.

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